EPILOC: A (WORKING) TEXT-BASED SYSTEM FOR PREDICTING PROTEIN SUBCELLULAR LOCATION
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
MOTIVATION: Predicting the subcellular location of proteins is an active research area, as a protein's location within the cell provides meaningful cues about its function. Several previous experiments in utilizing text for protein subcellular location prediction varied in methods, applicability and performance level. In an earlier work we have used a preliminary text classification system and focused on the integration of text features into a sequence-based classifier to improve location prediction performance. RESULTS: Here the focus shifts to the text-based component itself. We introduce EpiLoc, a comprehensive text-based localization system. We provide an in-depth study of text-feature selection, and study several new ways to associate text with proteins, so that text-based location prediction can be performed for practically any protein. We show that EpiLoc's performance is comparable to (and may even exceed) that of state-of-the-art sequence-based systems. EpiLoc is available at: http://epiloc.cs.queensu.ca.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it